SVC-enabled Popularity Prediction and Content Placement Based on Federated Learning

Confronted with the soaring growth of wireless traffic, edge caching has been deemed as a promising technique to address the challenges caused by redundant file deliveries and finite backhaul/fronthaul capacities. When making caching decisions, popularity prediction is preliminary but crucial. Con-sidering diversified viewing preferences of users, scalable video coding (SV C) is used to divide the file into multiple layers. The layers are used as caching units, and then delivered to users. Owing to this fact, we study the layer-based popularity prediction and content placement schemes. To protect users' privacy, a distributed framework based on federated learning is proposed to acquire the layer popularity. During the local training process, users are divided into privacy-sensitive and privacy-insensitive users. Taking users' privacy sensitivity into account, different types of nodes are nominated as edge nodes (EN s) for training, further reducing the risk of privacy leakage. The stacked auto encoder is used to extract features from both users and videos. By resorting to collaborative filtering, the features are used as inputs for predicting the layer popularity. Extensive experiments are conducted using a real dataset. Results reveal that, in contrast with the scheme that only provides basic viewing quality, the cache hit rate (CHR) is doubled by applying the SVC-based scheme when the cache size of each EN is set to 160 Mbits. We also find that the proposed algorithm improves the CHR by 55 % and decreases the transmission delay by 49 %, as compared to the Least Frequently Used (LFU) scheme.